Cited 19 times in
Age-adjusted Charlson comorbidity index is a significant prognostic factor for long-term survival of patients with high-risk prostate cancer after radical prostatectomy: a Bayesian model averaging approach.
DC Field | Value | Language |
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dc.contributor.author | 나군호 | - |
dc.contributor.author | 이주용 | - |
dc.contributor.author | 조강수 | - |
dc.contributor.author | 조남훈 | - |
dc.contributor.author | 최영득 | - |
dc.contributor.author | 홍성준 | - |
dc.date.accessioned | 2017-02-24T07:40:19Z | - |
dc.date.available | 2017-02-24T07:40:19Z | - |
dc.date.issued | 2016 | - |
dc.identifier.issn | 0171-5216 | - |
dc.identifier.uri | http://hdl.handle.net/22282913/146484 | - |
dc.description.abstract | PURPOSE: We investigated the long-term prognostic impact of age-adjusted Charlson comorbidity index (ACCI) on overall mortality (OM), cancer-specific mortality (CSM), and other-cause mortality (OCM) according to risk stratification in patients with prostate cancer who underwent radical prostatectomy. METHODS: Data from 542 patients who underwent radical prostatectomy between 1992 and 2006 were analyzed. The impact of preoperative variables including age, prostate-specific antigen, biopsy Gleason sum, clinical stage, and ACCI on OM, CSM, and OCM were analyzed according to risk groups, with a median follow-up of 101 months. RESULTS: Subjects were stratified into either the high-risk group (n = 241) or the non-high-risk group (n = 301). Cox proportional hazards model demonstrated that the ACCI was the only significant predictor for OM in all patients (hazard ratio, HR 1.41; 95 % confidence interval, CI 1.19-1.66), non-high-risk group (HR 1.45; 95 % CI 1.09-1.91), and high-risk group (HR 1.37; 95 % CI 1.11-1.69). In competing risk analysis, CSM was not associated with the ACCI in either risk group. However, the ACCI had a significant impact on OCM in both the non-high-risk (HR 1.55; 95 % CI 1.16-2.1) and high-risk groups (HR 1.60; 95 % CI 1.23-2.08). A Bayesian model averaging approach verified that the ACCI was the most powerful predictor for OM and OCM in the both high-risk and non-high-risk groups. CONCLUSIONS: A thorough assessment of comorbidities is mandatory in establishing prognoses, even when considering invasive treatment modalities in high-risk prostate cancer patients. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.format.extent | 849~858 | - |
dc.language | English, German | - |
dc.publisher | Springer-Verlag | - |
dc.relation.isPartOf | JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/2.0/kr/ | - |
dc.subject.MESH | Age Factors | - |
dc.subject.MESH | Aged | - |
dc.subject.MESH | Bayes Theorem | - |
dc.subject.MESH | Biomarkers, Tumor/blood* | - |
dc.subject.MESH | Comorbidity | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Kaplan-Meier Estimate | - |
dc.subject.MESH | Lymphatic Metastasis | - |
dc.subject.MESH | Male | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Neoplasm Grading | - |
dc.subject.MESH | Neoplasm Staging | - |
dc.subject.MESH | Predictive Value of Tests | - |
dc.subject.MESH | Prognosis | - |
dc.subject.MESH | Proportional Hazards Models | - |
dc.subject.MESH | Prostate-Specific Antigen/blood* | - |
dc.subject.MESH | Prostatectomy* | - |
dc.subject.MESH | Prostatic Neoplasms/blood | - |
dc.subject.MESH | Prostatic Neoplasms/mortality* | - |
dc.subject.MESH | Prostatic Neoplasms/pathology* | - |
dc.subject.MESH | Prostatic Neoplasms/surgery | - |
dc.subject.MESH | Retrospective Studies | - |
dc.subject.MESH | Risk Factors | - |
dc.title | Age-adjusted Charlson comorbidity index is a significant prognostic factor for long-term survival of patients with high-risk prostate cancer after radical prostatectomy: a Bayesian model averaging approach. | - |
dc.type | Article | - |
dc.publisher.location | Germany | - |
dc.contributor.college | College of Medicine | - |
dc.contributor.department | Dept. of Urology | - |
dc.contributor.googleauthor | Joo Yong Lee | - |
dc.contributor.googleauthor | Ho Won Kang | - |
dc.contributor.googleauthor | Koon Ho Rha | - |
dc.contributor.googleauthor | Nam Hoon Cho | - |
dc.contributor.googleauthor | Young Deuk Choi | - |
dc.contributor.googleauthor | Sung Joon Hong | - |
dc.contributor.googleauthor | Kang Su Cho | - |
dc.identifier.doi | 10.1007/s00432-015-2093-0 | - |
dc.contributor.localId | A01227 | - |
dc.contributor.localId | A03161 | - |
dc.contributor.localId | A03801 | - |
dc.contributor.localId | A03812 | - |
dc.contributor.localId | A04111 | - |
dc.contributor.localId | A04402 | - |
dc.relation.journalcode | J01283 | - |
dc.identifier.eissn | 1432-1335 | - |
dc.identifier.pmid | 26660495 | - |
dc.identifier.url | http://link.springer.com/article/10.1007/s00432-015-2093-0 | - |
dc.subject.keyword | Bayes theorem | - |
dc.subject.keyword | Comorbidity | - |
dc.subject.keyword | Mortality | - |
dc.subject.keyword | Prostatic neoplasms | - |
dc.subject.keyword | Risk | - |
dc.contributor.alternativeName | Rha, Koon Ho | - |
dc.contributor.alternativeName | Lee, Joo Yong | - |
dc.contributor.alternativeName | Cho, Kang Su | - |
dc.contributor.alternativeName | Cho, Nam Hoon | - |
dc.contributor.alternativeName | Choi, Young Deuk | - |
dc.contributor.alternativeName | Hong, Sung Joon | - |
dc.contributor.affiliatedAuthor | Rha, Koon Ho | - |
dc.contributor.affiliatedAuthor | Lee, Joo Yong | - |
dc.contributor.affiliatedAuthor | Cho, Kang Su | - |
dc.contributor.affiliatedAuthor | Cho, Nam Hoon | - |
dc.contributor.affiliatedAuthor | Choi, Young Deuk | - |
dc.contributor.affiliatedAuthor | Hong, Sung Joon | - |
dc.citation.volume | 142 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 849 | - |
dc.citation.endPage | 858 | - |
dc.identifier.bibliographicCitation | JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY, Vol.142(4) : 849-858, 2016 | - |
dc.date.modified | 2017-02-24 | - |
dc.identifier.rimsid | 40966 | - |
dc.type.rims | ART | - |
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